Compare occupancy and empty travel

Hold travel service fixed while changing occupancy

Compare all three vehicle packets in the dispersed scenario. Each serves 120 one-way ten-kilometer requests, but the target loads differ: two passengers in the small vehicle, three in the minibus and five in the bus. Predict whether the largest vehicle must have the lowest scenario operational burden per delivered service.

The supplied factors are 0.2, 0.4 and 1.2 kg CO2e per vehicle-km respectively. They are original illustrative constants, not measured fleets, fuel labels or full lifecycle factors. With sixty, forty and twenty-four loaded trips, the totals are 120, 160 and 288 kg. At this low supplied occupancy, the small vehicle has the lowest of these three scenario totals.

Observe a ranking reversal under explicit assumptions

Now choose consolidated occupancy, retaining the same request. The target loads become two, eight and thirty. Loaded-trip counts are sixty, fifteen and four; scenario totals become 120, 60 and 48 kg. All requests are still served, so the ranking reversal does not come from silently dropping passengers.

Capture the bus in both scenarios and show its vehicle-distance calculation: 24 × 10 = 240 km versus 4 × 10 = 40 km. With its fixed 1.2 factor, burden changes from 288 to 48 kg. The comparison changes a stipulated load pattern. It does not establish that a new vehicle purchase will produce that consolidation or a universal technology ranking.

Include empty travel in the correct denominator

Keep the consolidated bus but add the empty-return scenario. Each of four loaded trips now has 10 km of empty travel. Total vehicle distance rises from 40 to 80 km, and scenario operational CO2e doubles from 48 to 96 kg. Served passenger distance remains 1,200 because an empty return carries no passengers.

The loaded-trip mean remains thirty and loaded-seat utilization remains 75%. Average passenger load across all vehicle distance instead falls from 1,200/40 = 30 to 1,200/80 = 15. Those two occupancy measures answer different questions. Empty running does not remove physical seats or reduce passengers already carried.

The extra distance does not automatically reduce the model’s trip cap because no time budget is implemented. A real schedule would need that feasibility check. Construction, upstream fuel or electricity supply, maintenance and end-of-life also remain outside the supplied operational factor.

Audit a historical definition before using its numbers

The FHWA report’s Exhibit 4-23 defines average occupancy using passenger miles divided by vehicle revenue miles. Heavy rail rises from 23.0 in 2004 to 27.9 in 2014, a difference of 4.9. The fixed-route bus grouping rises from 10.0 to 11.1, a difference of 1.1; that grouping includes bus, commuter bus and bus rapid transit.

These are rounded historical national summary ratios, not unique-person counts, seat utilization or current local loads. Revenue service can run empty, so revenue distance is not identical to our loaded-distance variable. Deadhead distance is not in the published denominator. The four values cannot identify our model factors, an emissions change or its cause.

Transfer: repair an occupancy comparison

A fictional service carries 600 passenger-km using 40 loaded vehicle-km and 20 empty vehicle-km. Find loaded-distance and all-distance passenger loads. Explain why comparing one of these with an unrelated revenue-distance statistic needs additional definition checks.